When AI Agents Forget What They Saw: The Goal Drift Problem in Video Research
Author(s): Kaushik Rajan Originally published on Towards AI. Why more autonomy doesn’t always mean better performance, and what the first video deep research benchmark reveals about the limits of agentic AI You’re watching a museum tour video. Someone asks: “What’s the registration …
Go vs Python vs TypeScript — Which is The Most Efficient in LLM-Assisted Programming?
Author(s): Vikas Tiwari Originally published on Towards AI. Source: Image by the author TypeScript is the most popular language when it comes to LLM assisted programming. In the last year or so TypeScript has been the go to language when it comes …
Build a Secure Sandbox for Your AI Agent
Author(s): Digvijay Mahapatra Originally published on Towards AI. Stop clicking “Approve” for every shell command. Here is how to build true architectural autonomy. Approval fatigue is the enemy of security. Learn to build a secure Docker sandbox for your AI agent that …
Top 30 XGBoost Interview Questions and Answers (Part 2 of 2)
Author(s): Shahidullah Kawsar Originally published on Towards AI. Machine Learning Interview Preparation Part 09 Solution XGBoost is a machine learning method used to make accurate predictions. It works by building many small decision trees one after another, where each new tree focuses …
VI. FastAPI Dependency Injection: The Clean Code Secret
Author(s): Mahimai Raja J Originally published on Towards AI. Dependency Injection sounds standard? In FastAPI, it’s practical magic Today, we will see how to use dependency injection efficiently. I would refere dependency injection as DI sometimes and is more commanly used in …
AI Agents in 2026: The Data Problem No One Mentions
Author(s): Ahmed M. Abdelfattah Originally published on Towards AI. Why vendors promise 3–5 employee productivity but Forrester finds 0% improvement and what your data infrastructure needs before deployment works Google Cloud claims AI agents deliver productivity equivalent to hiring 3–5 employees. Forrester’s …
AI’s Next Strategic Phase: From Lab Curiosity to Core Economy Driver
Author(s): Vivek Acharya Originally published on Towards AI. AI’s Next Strategic Phase: From Lab Curiosity to Core Economy Driver AI is undergoing a profound strategic shift. Not long ago, success in AI was measured by flashy model demos and incremental accuracy gains. …
Reference Architecture for Private AI on Azure: Designing Secure, Compliant, Hybrid LLM Systems
Author(s): Sandip Patel Originally published on Towards AI. Introduction: The Rise of Private AI Over the past two decades working in cloud architecture, I’ve witnessed several technology waves, but Private AI marks a fundamental shift in how enterprises will operate for the …
Understanding Retrieval Augmented Generation in The Easiest Way
Author(s): Asjad Abrar Originally published on Towards AI. Understanding Retrieval Augmented Generation in The Easiest Way The landscape of artificial intelligence has witnessed remarkable transformations over the past few years, with large language models demonstrating unprecedented capabilities in natural language understanding and …
Meta’s Reckoning: $73B in Metaverse Losses, an AI Talent Exodus, and Zuckerberg’s $14B Reset
Author(s): Zoom In AI Originally published on Towards AI. Reality Labs is shrinking. Meta’s AI org has been rattled by a benchmark controversy. And a geopolitically sensitive AI-agent deal is now under regulatory scrutiny. The pivot is real. The question is whether …
Why Your Brilliant AI Agent Might Be Your Biggest Risk (And How to Fix That)
Author(s): MahendraMedapati Originally published on Towards AI. Why Your Brilliant AI Agent Might Be Your Biggest Risk (And How to Fix That) Picture this: You’ve just deployed a shiny new AI agent to handle customer orders. It’s fast, it’s smart, and for …
I Let an Autonomous Agent Refactor My Legacy Codebase. The Result Was Terrifying.
Author(s): Adi Insights and Innovations Originally published on Towards AI. We spent 3 years afraid to touch utils_final_v2.js. So, I gave the keys to an AI agent with shell access. Here is how I built it. We all have That Module. This …
Part 2: Your AI Agent is Only as Good as Its Tools
Author(s): Rittika Jindal Originally published on Towards AI. This is Part 2 of a 4-part series: “The Honest Guide to Building AI Agents That Actually Work” In Part 1, we solved the context loss problem. Structured artifacts, progress logs, session protocols — …
Visualizing Risk: A Latent World Model for Financial Crisis Hedging
Author(s): Chase Metoyer Originally published on Towards AI. Introduction Financial markets have traditionally been understood through parametric models and stochastic calculus. From Black-Scholes to Heston, quantitative finance relies on mathematical frameworks that treat volatility either as a scalar parameter or as an …
![Why Recommendation Systems Are Structurally Different from Deep Learning[1/2]](https://miro.medium.com/v2/resize:fit:653/1*uMKlK0Fp87Hh1jtBdlKzQA.png "Why Recommendation Systems Are Structurally Different from Deep Learning[1/2]")
Why Recommendation Systems Are Structurally Different from Deep Learning[1/2]
Author(s): NP_123 Originally published on Towards AI. Why One-Hot Features and Naïve MLPs Fail This article is Part 1 of a two-part series on why recommendation systems require fundamentally different modeling assumptions from standard deep learning. 📌 TL;DR Recommendation systems are not …
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